DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This non-final office action is in response to the application filed 23 February 2024.
Claims 1-8 are pending. Claim 1 is an independent claim.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d).
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 29 February 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The examiner accepts the drawings filed 23 February 2024.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-8 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
With respect to claims 1-8, the claims are generally narrative and indefinite, failing to conform with current U.S. practice. They appear to be a literal translation into English from a foreign document and are replete with grammatical and idiomatic errors. The examiner has attempted to provide a listing below of indefinite elements within the claims.
Additionally, it is noted that dependent claims fail to cure the deficiencies of those claims upon which the depend. These dependent claims incorporate the rejection of the claims upon which the depend by reference.
With respect to claims 1-2, 4 and 8, the claims recites the limitation "FDL-based" and “FDL”. While independent claim 1 references “federated deep learning,” claims 1-2, 4, and 8 do not indicate that “FDL” is intended to reference “federated deep learning.” Therefore, there is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation "the basis" in line 4. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation "the regions" in line 4. There is insufficient antecedent basis for this limitation in the claim.
Claim 2 recites the limitation "the FDL clients" in line 7. While the claim supports “selected FDL clients (line 4)” and “the selected FDL clients (line 6),” the claim does not provide antecedent basis for “the FDL clients (line 7).” There is insufficient antecedent basis for this limitation in the claim.
With respect to claim 3, the term “well-trained model” in line 2 is a relative term which renders the claim indefinite. The term “well-trained model” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
With respect to claim 4, the term “should be guaranteed” in line 6 is a relative term which renders the claim indefinite. The term “should be guaranteed” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claim 4 recites the limitation "the selection ratio" in line 7. There is insufficient antecedent basis for this limitation in the claim.
Claim 4 recites the limitation "the FDL client set" in line 8. There is insufficient antecedent basis for this limitation in the claim.
Claim 4 recites the limitation "the function max" in line 8. There is insufficient antecedent basis for this limitation in the claim.
Claim 5 recites the limitation "the main predictive metric" in line 3. There is insufficient antecedent basis for this limitation in the claim.
Claim 5 recites the limitation "the CPU usage" in line 4. There is insufficient antecedent basis for this limitation in the claim.
Claim 6 recites the limitation "the interval average" in line 3. There is insufficient antecedent basis for this limitation in the claim.
Claim 6 recites the limitation "the invalid load data" in line 4. There is insufficient antecedent basis for this limitation in the claim.
With respect to claim 6, the term “important features of raw load data” in line 6 is a relative term which renders the claim indefinite. The term “important features of raw load data” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claim 6 recites the limitation "the convergence of local models" in lines 6-7. There is insufficient antecedent basis for this limitation in the claim.
Claim 6 recites the limitation "the range [0,1]" in lines 8-9. There is insufficient antecedent basis for this limitation in the claim.
Claim 7 recites the limitation "the interference of noise" in line 4. There is insufficient antecedent basis for this limitation in the claim.
Claim 7 recites the limitation "the center point" in line 6. There is insufficient antecedent basis for this limitation in the claim.
Claim 7 recites the limitation "the window length" in line 6. There is insufficient antecedent basis for this limitation in the claim.
Claim 7 recites the limitation "the length" in lines 6-7. There is insufficient antecedent basis for this limitation in the claim.
Claim 7 recites the limitation “the elements before and after fitting" in line 10. There is insufficient antecedent basis for this limitation in the claim.
Claim 7 recites the limitation “the elements fitting" in line 12. There is insufficient antecedent basis for this limitation in the claim.
Claim 7 recites the limitation “the window" in line 12. There is insufficient antecedent basis for this limitation in the claim.
Claim 7 recites the limitation “the fitting parameter" in line 16. There is insufficient antecedent basis for this limitation in the claim.
With respect to claim 7, the term “a sliding-window way” in line 17 is a relative term which renders the claim indefinite. The term “a sliding window way” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claim 8 recites the limitation “the preprocessed load data X’" in line 3. There is insufficient antecedent basis for this limitation in the claim.
Claim 8 recites the limitation “the difference term" in line 8. There is insufficient antecedent basis for this limitation in the claim.
Claim 8 recites the limitation “the client-drift degree" in line 8. There is insufficient antecedent basis for this limitation in the claim.
Claim 8 recites the limitation “the update direction" in line 9. There is insufficient antecedent basis for this limitation in the claim.
With respect to claim 8, the term “correct the update direction of local models in time (emphasis added)” in line 9 is a relative term which renders the claim indefinite. The term “correct the update direction of local model in time” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
With respect to claim 8, the claim is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential elements, such omission amounting to a gap between the elements. See MPEP § 2172.01. The claim recites the equation:
w
k
←
w
k
-
ƞ
(
∇
l
k
w
k
:
b
-
c
w
+
c
However, the claim fails to define the essential elements,
∇
l
k
and b.
Claim 8 recites the limitation “the learning rate" in line 13. There is insufficient antecedent basis for this limitation in the claim.
With respect to claim 8, the claim is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential elements, such omission amounting to a gap between the elements. See MPEP § 2172.01. The claim recites the equation:
w
←
Σ
k
=
1
K
n
k
n
w
k
,
c
←
Σ
k
=
1
K
n
k
n
c
k
However, the claim fails to define the essential elements,
n
k
and n.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
With respect to independent claim 1, the claims recite a “cooperative universal framework for load prediction with personalized federated deep learning (lines 1-2).” The claimed framework does not define a process, machine, manufacture, or composition of matter. For this reason, the claim does not fall within at least one of the four categories of patent eligible subject matter.
Claims 2-8 fail to cure the deficiencies of independent claim 1. Claims 2-8 are rejected under similar rationale.
Claims 1-8 rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1:
According to Step 1 of the two Step analysis, claims 1-8 are directed toward a “cooperative universal framework for load prediction with personalized federated deep learning.” As noted above, these claims fail to define a process, machine, manufacture, or composition of matter and are non-statutory under Step 1. However, in order to practice compact prosecution, these claims will be further analyzed under Steps 2A and 2B.
Claim 1:
Step 2A, Prong 1:
The claim recites:
using site IDS of edge servers as the basis for dividing the regions of cooperative training (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation of site IDs of edge servers for dividing the regions for cooperative training)
the edge servers within a same site are regarded as clients in FDL (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation that edge servers are at the same site and treating them as clients)
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional elements:
adopting an FDL-based cooperative training manner in multi-edge environments
the edge servers conduct cooperative training with a parameter server in FDL
The training is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim recites the additional elements:
adopting an FDL-based cooperative training manner in multi-edge environments
the edge servers conduct cooperative training with a parameter server in FDL
The training is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 2:
With respect to claim 2, the claim depends upon claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
elect FDL clients to participate in a current round of federated aggregation (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to select clients to participate in aggregation)
the selected… participate in the current round of federated aggregation (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to perform aggregation)
each… preprocesses the historical load data stored on its edge server including cleaning, resampling, normalization, feature extraction, and denoising (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to preprocess historical load data to clean, resample, normal, extract features, and denoise data, with the aid of pencil and paper)
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional elements:
the parameters server
selected FDL clients
each FDL client
The additional elements of an interface and a servers and clients, which are recited at a high-level of generality, amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The claim recites the additional element:
the FDL clients receive a global model and control parameters distributed by the parameter server, and then starts local training with historical loads
The additional elements amounts to a data gathering step, which is recited at a high level of generality, and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The claim recites the additional elements:
guided by the global model, the global control parameters, and local control parameters, each FDL client trains and updates its local load prediction model through multiple epochs of training validation, and testing
after all selected FDL clients complete their local training and upload their local models and control parameters, the parameter server will make aggregation
the parameter server updates the global model and control parameters and then starts a next round of FDL cooperative training
The training is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim recites the additional elements:
the parameters server
selected FDL clients
each FDL client
The additional elements of an interface and a servers and clients, which are recited at a high-level of generality, amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The claim recites the additional element:
the FDL clients receive a global model and control parameters distributed by the parameter server, and then starts local training with historical loads
The additional elements amounts to a data gathering step, which is recited at a high level of generality, and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The claim recites the additional elements:
guided by the global model, the global control parameters, and local control parameters, each FDL client trains and updates its local load prediction model through multiple epochs of training validation, and testing
after all selected FDL clients complete their local training and upload their local models and control parameters, the parameter server will make aggregation
the parameter server updates the global model and control parameters and then starts a next round of FDL cooperative training
The training is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 3:
With respect to claim 3, the claim depends upon claim 2. The analysis of claim 2 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
predict future edge loads (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a judgement to predict future edge loads)
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional elements:
a well-trained model is used
The use of a model is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim recites the additional elements:
a well-trained model is used
The use of a model is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 4:
With respect to claim 4, the claim depends upon claim 2. The analysis of claim 2 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
according to the selection ratio C, the parameter server randomly selects max (C ● K, 1) FDL clients from all K FDL clients to participate in the federated aggregation, and the FDL client set S is build, wherein the function max (,) is used to avoid training interruptions if no FDL client is selected (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to randomly select clients for participation in aggregation based upon an evaluated maximum number of clients)
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional elements:
a parameter server initializes a global mode w, global parameters c, and number of clients K
each FDL client conducts model training by utilizing local load data, which contains runtime information of resource usable on the edge servers
The training of a model is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
The claim recites the additional elements:
each FDL client maintains its local control parameters
c
k
, both c and
c
k
are initialized by 0, where c =
Σ
k
=
1
K
c
k
should be guaranteed
The additional element amounts to storing information in memory, which is recited at a high level of generality, and amounts to extra-solution activity of receiving data i.e. pre-solution activity of storing and gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The claim recites the additional elements:
for each FDL communication round
the parameter server distributes the global model w and global control parameters c to each FDL client in S
for each FDL client k (k ∈ S), it will receive the global model and global control parameters (w, c) and update its local model and local control parameters (
w
k
,
c
k
)
As discussed above, the additional elements of data gathering, which is recited at a high level of generality, and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim recites the additional elements:
a parameter server initializes a global mode w, global parameters c, and number of clients K
each FDL client conducts model training by utilizing local load data, which contains runtime information of resource usable on the edge servers
The training of a model is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
The claim recites the additional elements:
each FDL client maintains its local control parameters
c
k
, both c and
c
k
are initialized by 0, where c =
Σ
k
=
1
K
c
k
should be guaranteed
The additional element amounts to storing information in memory, which is recited at a high level of generality, and amounts to extra-solution activity of receiving data i.e. pre-solution activity of storing and gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The claim recites the additional elements:
for each FDL communication round
the parameter server distributes the global model w and global control parameters c to each FDL client in S
for each FDL client k (k ∈ S), it will receive the global model and global control parameters (w, c) and update its local model and local control parameters (
w
k
,
c
k
)
As discussed above, the additional elements of data gathering, which is recited at a high level of generality, and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 5:
With respect to claim 5, the claim depends upon claim 4. The analysis of claim 4 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
CPU usage is used as the main prediction metric (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to perform a prediction based upon CPU usage)
for an edge server,
x
t
indicates the CPU usage at t, and a time-series sequence of loads donates as X = {
x
1
,
x
2
, …,
x
n
} (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to perform a prediction based upon CPU usage, where the time-series sequence is evaluated)
Step 2A, Prong 2:
The claim fails to recite additional elements considered under Step 2A, Prong 2.
Step 2B:
The claim fails to recite additional elements considered under Step 2B.
Claim 6:
With respect to claim 6, the claim depends upon claim 5. The analysis of claim 5 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
invalid load data is replace by taking the interval average during the preprocessing process and meanwhile compress the invalid load data by resampling, which can extract important features of raw load data (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to identify an invalid load and replacing the value with the interval average)
normalize raw loads, aiming to speed up the convergence of local models (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to perform a normalization of the raw load)
after normalization, the raw load data is mapped to the range [0,1], and the preprocessing process is defined as ˜X =
X
-
X
m
i
n
X
m
a
x
-
X
m
i
n
where X indicates the raw load data,
X
m
a
x
and
X
m
i
n
are a maximum and a minimum CPU usage in the raw load data, respectively (mathematical concept; As drafted, this limitation encompasses a Mathematical Concept (mathematical relationships, mathematical formulas or equations, or mathematical calculations). The mathematical concept defines the ratio of the CPU usage compared the difference between the maximum and minimum CPU usage)
Step 2A, Prong 2:
The claim fails to recite additional elements considered under Step 2A, Prong 2.
Step 2B:
The claim fails to recite additional elements considered under Step 2B.
Claim 7:
With respect to claim 7, the claim depends upon claim 6. The analysis of claim 6 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
a Savitzky-Golay (SG) based filter is used to smooth the raw load data, aiming to minimize the interference of noise (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to apply a filter to raw load data)
define a sub-sequence of ~X as
S
q
= {~
~
X
q
-
w
,
~
X
q
-
w
+
1
, …,
~
X
q
, …
~
X
q
-
w
-
1
,
~
X
q
+
w
} where
S
q
⊂~X, q is the center point of
S
q
, w is half the window length, and the length of
S
q
is (2w + 1) (mathematical concept; As drafted, this limitation encompasses a Mathematical Concept (mathematical relationships, mathematical formulas or equations, or mathematical calculations). The mathematical concept defines a sequence of values)
perform fitting for each element in
S
q
by
X
'
q
+
k
=
∑
i
=
0
γ
α
i
~
X
q
+
k
i
where ~x and x’ are the elements before and after fitting, respectively (mathematical concept; As drafted, this limitation encompasses a Mathematical Concept (mathematical relationships, mathematical formulas or equations, or mathematical calculations). The mathematical concept performs fitting for each element by applying a summation)
moreover,
α
is a fitting parameters and
γ
is a polynomial number (mathematical concept; As drafted, this limitation encompasses a Mathematical Concept (mathematical relationships, mathematical formulas or equations, or mathematical calculations). The mathematical concept defines the fitting parameters and a polynomial number)
realize the element fitting the window by solving a system of
γ
element linear equations (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to realize the element fitting window by performing an evaluation)
we first build a system of
γ
element linear equations by forming (2w + 1) equations from the elements in
S
q
(mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to identify/build a system of linear equations)
a least squared method is used to determine the fitting parameter
α
(mathematical relationships, mathematical formulas or equations, or mathematical calculations). The mathematical concept defines application of a least square method to fit a parameter)
a proposed SG-based filter will work in a sliding-window way until all load data is preprocessed (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to apply a filter)
Step 2A, Prong 2:
The claim fails to recite additional elements considered under Step 2A, Prong 2.
Step 2B:
The claim fails to recite additional elements considered under Step 2B.
Claim 8:
With respect to claim 8, the claim depends upon claim 7. The analysis of claim 7 is incorporated herein by reference.
Step 2A, Prong 1:
The claim recites:
the preprocessed load data X’ is split into batches to serve as local training data for FDL clients (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an judgement to determine splitting load data into batches)
the process of the local model update is defined as
w
k
←
w
k
-
ƞ
(
∇
l
k
w
k
:
b
-
c
w
+
c
where the difference term (
c
w
-
c
) is used to estimate the client-drift degree and to correct the update direction of local models in time (mathematical relationships, mathematical formulas or equations, or mathematical calculations). The mathematical concept defines estimate of a client-drift degree based upon a calculated value)
after completing local training, the local control parameters are updated and the update process is defined as
c
k
←
c
k
-
c
+
1
E
ƞ
(
w
-
w
k
)
where
ƞ
is the learning rate (mathematical relationships, mathematical formulas or equations, or mathematical calculations). The mathematical concept defines the updating of parameters based upon a calculated learning rate)
the update of control parameters
c
k
is considered the global control parameters c and the difference between the global model and updated local model, denoted by
(
w
-
w
k
)
(mathematical relationships, mathematical formulas or equations, or mathematical calculations). The mathematical concept defines the updating of parameters based upon a calculated learning rate)
the parameter server updates the global model w and global control parameters c and then starts the next round of FDL training an update process is defined as
w
←
Σ
k
=
1
K
n
k
n
w
k
,
c
←
Σ
k
=
1
K
n
k
n
c
k
(mathematical relationships, mathematical formulas or equations, or mathematical calculations). The mathematical concept defines the updating of parameters based upon a calculated learning rate)
Step 2A, Prong 2:
The judicial exception is not integrated into a practical application.
The claim recites the additional element:
each FDL client performs E epochs for training its local model
Training is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
The claim recites the additional elements:
the FDL client k uploads the updated
w
k
and
c
k
to the parameter server
after receiving the updated parameters of all participated clients
As discussed above, the additional elements amounts to a data gathering step which is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim recites the additional element:
each FDL client performs E epochs for training its local model
Training is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
The claim recites the additional elements:
the FDL client k uploads the updated
w
k
and
c
k
to the parameter server
after receiving the updated parameters of all participated clients
As discussed above, the additional elements amounts to a data gathering step which is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 1 is rejected under 35 U.S.C. 103 as being unpatentable over Anand et al. (US 12705497, 371 date 20 April 2023, hereafter Amand) and further in view of Hu et al. (US 2025/0209383, filed 9 September 2020, hereafter Hu).
As per independent claim 1, Anand discloses a multi-edge cooperative universal framework for load prediction with personalized federated learning, comprising:
adopting a federated learning based cooperative training manner in multi-edge environments, using edge devices as the basis for dividing the regions of cooperative training (column 2, lines 19-36: Here, a federated learning approach is used to train a model based upon local training datasets, each training dataset being located at a respective edge device)
the edge servers within a same site are regarded as clients in federated learning, and the edge servers conduct cooperative training with a parameter server in federated learning (Figure 1a; column 8, line 50- column 9, line 59: Here, a plurality of edge devices (items 101 and 102) act as clients in a federate learning system (item 100). The edge devices each perform training on their respective datasets (items 181 and 182). These edge devices provide model updates (items 161 and 162) to the aggregation device (item 109) to update the current federated model. Further, this aggregation device may be implemented in one or more edge devices (column 9, lines 20-22). The examiner interprets the aggregation device as defining an edge server because it may be implemented at an edge device and it provides/serves the federated learning models to the edge devices)
Amand fails to specifically fails to disclose:
federated deep learning
site IDs of edge servers
However, Hu, which is analogous to the claimed invention because it is directed toward federated learning, discloses federated deep learning (paragraph 0023: Here federated learning may be used to train a deep learning model without transmitting a large volume of training data). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Hu with Anand, with a reasonable expectation of success, as it would have allowed for training a deep learning model without transmitting large volumes of data while protecting data security and data privacy (Hu: paragraph 0023).
Additionally, the examiner takes official notice that it was notoriously well-known in the art at the time of the applicant’s effective filing date to use site IDs of servers, such as URLs, URIs, and/or IP address, for identifying and accessing data at a server. It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined the well-known with Anand-Hu, with a reasonable expectation of success, as it would have allowed for identifying a server and accessing data from the server.
Claims 2-3 are rejected under 35 U.S.C. 103 as being unpatentable over Anand and Hu and further in view of Wang (US 2024/0424199, filed 3 August 2023).
As per dependent claim 2, Anand and Hu disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Anand discloses the federated learning cooperative training further comprising the following steps:
the parameter server selects federated learning clients to participate in a current round of federated aggregation (Figure 1a; column 9, lines 6-19: Here, the set of edge devices to which the model is sent may vary each iteration. This variance indicates that a selection is made to identify clients to participate in the current round of aggregation)
the selected federated learning clients participate in the current round of federated aggregation (Figure 1a; column 8, line 50- column 9, line 19: Here, the current federated model is sent to one or more edge devices. Training is performed until a stopping criterion is reached, such as convergences and achievement of an optimal performance)
the federated learning clients receive a global model and control parameters distributed by the parameter server, and then starts local training with historical loads (Figure 1a; column 8, line 50-column 9, line 59: Here, the edge devices (clients) (items 101 and 102) receive the current global model (item 160) including parameters (column 18, line 41-43). Local training is performed using historical data, local training dataset (items 181 and 182))
guided by the global mode, global control parameters, the local control parameters, each federated learning client trains and updates its local load prediction model through multiple epochs of training, validation, and testing (Figure 1a; column 8, line 50- column 9, line 19: Here, the current federated model is sent to one or more edge devices. Training is performed until a stopping criterion is reached, such as convergences and achievement of an optimal performance. The examiner interprets identifying convergence and achievement of optimal performance as validating and testing the model)
after all selected federated learning clients complete their local training and upload their local models, and control parameters, the parameter server will make aggregation (column 9, line 60- column 10, line 12: Here, an aggregation device receives the local models and control parameters for aggregation)
the parameter server updates the global model and control parameters and then starts a next round of federated cooperative training (column 8, line 54- column 9, line 5: Here, the training is performed until an aggregation device, parameter server, determines model convergences at an optimal performance)
Anand fails to specifically disclose:
each federated learning client preprocesses the historical load data stored on the edge server including cleaning, resampling, normalization, feature extraction, and denoising
However, Wang, which is analogous to the claimed invention because it is directed toward preprocessing data, discloses:
a client preprocesses the data including cleaning (paragraph 0056), resampling (paragraph 0056), normalization (paragraph 0046), feature extraction (paragraph 0048), and denoising (paragraph 0046)
It would have been obvious to one of ordinary skill in the art at the time of the applicant effective filing date to have combined Wang with Anand-Hu, with a reasonable expectation of success, as it would have allowed for optimizing data for use in a deep learning model by preprocessing the data (Wang: paragraphs 0046 and 0053).
As per dependent claim 3, Anand, Hu, and Wang disclose the limitations similar to those in claim 2, and the same rejection is incorporated herein. Anand disclose wherein a well-trained model is used to predict future edge loads (column 3, lines 29-56: Here, an edge device may perform filtering based upon a high load and update the model locally at an available time).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Sahin et al. (US 12572799): Discloses selecting a plurality of edge devices in federated edge learning (claim 1)
Roth et al. (US 2022/0366220): Discloses selecting a ratio of local models in a federated learning environment for training the global model at a federated server (paragraph 0078)
Kim et al. (US 2023/0018893): Discloses a distributed federated learning technique (paragraph 0019) that identifies a ratio of current local models to be used in training (paragraph 0048)
White et al. (US 2024/0305535): Discloses a minimum and maximum CPU load capacity estimate (paragraph 0211) and a validity of the load estimates (paragraph 0271)
Chen et al. (US 2015/0233998): Discloses use of a Savitzky-Golay smoothing filter to minimize noise (paragraph 0087)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE R STORK whose telephone number is (571)272-4130. The examiner can normally be reached 8am - 2pm; 4pm - 6pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez Rivas can be reached at 571/272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KYLE R STORK/Primary Examiner, Art Unit 2128
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